IEEE Trans Image Process - Bayesian texture classification based on contourlet transform and BYY harmony learning of Poisson mixtures.

Tópicos

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Resumo

As a newly developed 2-D extension of the wavelet transform using multiscale and directional filter banks, the contourlet transform can effectively capture the intrinsic geometric structures and smooth contours of a texture image that are the dominant features for texture classification. In this paper, we propose a novel Bayesian texture classifier based on the adaptive model-selection learning of Poisson mixtures on the contourlet features of texture images. The adaptive model-selection learning of Poisson mixtures is carried out by the recently established adaptive gradient Bayesian Ying-Yang harmony learning algorithm for Poisson mixtures. It is demonstrated by the experiments that our proposed Bayesian classifier significantly improves the texture classification accuracy in comparison with several current state-of-the-art texture classification approaches.

Resumo Limpo

newli develop d extens wavelet transform use multiscal direct filter bank contourlet transform can effect captur intrins geometr structur smooth contour textur imag domin featur textur classif paper propos novel bayesian textur classifi base adapt modelselect learn poisson mixtur contourlet featur textur imag adapt modelselect learn poisson mixtur carri recent establish adapt gradient bayesian yingyang harmoni learn algorithm poisson mixtur demonstr experi propos bayesian classifi signific improv textur classif accuraci comparison sever current stateoftheart textur classif approach

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